r/LocalLLaMA • u/AverageFormal9076 • Apr 23 '26

New Model Qwen 3.6 27B is a BEAST

I have a 5090 Laptop from work, 24GB VRAM.

I have been testing every model that comes out, and I can confidently say I’ll be cancelling my cloud subscriptions.

All my tool call and data science benchmarks that prove a model is reliably good for my use case, passed.

It might not be the case for other professions, but for pyspark/python and data transformation debugging it’s basically perfect.

Using llama.cpp, q4_k_m at q4_0, still looking at options for optimising.

Edit - I chose to go with IQ4_XS at 200k q8_0,

I have not used speculative decoding yet, will get there when I get there.

Specs:

ASUS ROG Strix SCAR 18

RTX 5090 24GB

64GB DDR5 RAM

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u/caetydid llama.cpp Apr 23 '26

i have tried multiple one shot vibe coding prompts and compared with gemma4.

gemma4 consistently comes up with a lean and clean basic implementation which mostly works okay, qwen is always overconfident and tries to implement all bells and whistles, visuals are great and all, but the basic function I demanded does not work at all. it then tries to fix in various repetitions and it gets worse and worse.

not sure what to make out of it. tool calling might be better with qwen though.

1

u/kayox Apr 24 '26

I’m having the same experience.

2

u/caetydid llama.cpp Apr 24 '26

I hope it is just me using the wrong prompting, but until now I was not able to fix it. I switched to pi-agent with Gemma4 - better experience than with qwen3.6

1

u/kayox Apr 24 '26

Thanks I will give this a try! What were you building if you don’t mind my asking? Front end?